Supplementary Data from Benefit of Complete Response in Multiple Myeloma Limited to High-Risk Subgroup Identified by Gene Expression Profiling
Jeffrey HaesslerJohn D. ShaughnessyFenghuang ZhanJohn CrowleyJoshua EpsteinFrits van RheeElias AnaissieMauricio Pineda‐RomanMaurizio ZangariKlaus HollmigAbid MohiuddinYazan AlsayedAntje HoeringGuido TricotBart Barlogie
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Supplementary Data from Benefit of Complete Response in Multiple Myeloma Limited to High-Risk Subgroup Identified by Gene Expression ProfilingKeywords:
Profiling (computer programming)
Subgroup analysis
Hierarchical clustering
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Profiling (computer programming)
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Host response
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Microarrays have been used to evaluate the expression of thousands of genes in various tissues. However, few studies have investigated the change in gene expression profiles in one of the most easily accessible tissues, whole blood. We utilized an acute inflammation model to investigate the possibility of using a cDNA microarray to measure the gene expression profile in the cells of whole blood. Blood was collected from male Sprague-Dawley rats at 2 and 6 h after treatment with 5 mg/kg (ip) LPS. Hematology showed marked neutrophilia accompanied by lymphopenia at both time points. TNF-alpha and IL-6 levels were markedly elevated at 2 h, indicating acute inflammation, but by 6 h the levels had declined. Total RNA was isolated from whole blood and hybridized to the National Institute of Environmental Health Sciences Rat Chip v.3.0. LPS treatment caused 226 and 180 genes to be differentially expressed at 2 and 6 h, respectively. Many of the differentially expressed genes are involved in inflammation and the acute phase response, but differential expression was also noted in genes involved in the cytoskeleton, cell adhesion, oxidative respiration, and transcription. Real-time RT-PCR confirmed the differential regulation of a representative subset of genes. Principal component analysis of gene expression discriminated between the acute inflammatory response apparent at 2 h and the observed recovery underway at 6 h. These studies indicate that, in whole blood, changes in gene expression profiles can be detected that are reflective of inflammation, despite the adaptive shifts in leukocyte populations that accompany such inflammatory processes.
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We measured the expression levels of 450 genes during mouse postnatal cerebellar development by quantitative PCR using RNA purified from layers of the cerebellar cortex. Principal component analysis of the data matrix demonstrated that the first and second components corresponded to general levels of gene expression and gene expression patterns, respectively. We introduced 288 of the 450 genes into PC12 cells using a high-throughput transfection assay based on atelocollagen and determined the ability of each gene to promote neurite outgrowth or cell proliferation. Five genes induced neurite outgrowth, and seven genes enhanced proliferation. Evaluation of the functional data and gene expression patterns showed that none of these genes exhibited elevated expression at maturation, suggesting that genes characteristic of mature neurons are not likely to participate in neuronal development. These results demonstrate that functional data can facilitate interpretation of expression profiles and identification of new molecules that participate in biological processes.
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Aging and aging-related diseases are associated with altered patterns of gene expression, involving quantitative and qualitative changes in the abundance of specific transcripts. A complete and simultaneous analysis of gene expression should therefore lead to important insights into the transcriptional mechanisms underlying the aging process. Recently, we have employed high-throughput gene expression profiling to study transcriptional activity in heart. Two technologies, serial analysis of gene expression (SAGE) and gene expression arrays, allow rapid, large-scale expression profiling, which provides information about the dynamics of total gene expression with age and which can be employed to identify candidate genes that may serve as diagnostic and prognostic markers in age-associated cardiac diseases. The accompanying gene predictions from high-throughput gene expression profiling provide a starting point for understanding the function, the complexity of interactions, and the role of genes in promoting cellular/organismal phenotypes during senescence and disease. In this review we describe the current state of transcriptome profiling by SAGE and microarrays and discuss how results generated with these approaches in heart can be applied to the study of aging and the treatment of cardiovascular diseases.
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Gene expression fingerprints are already a useful classification tool in tumor biology or drug application. PURPOSE: Similarly, gene expression profiling may provide information for the characterization and understanding of the immunological response to exercise. Microarray expression analysis affords the opportunity to find specific gene expression pattern (fingerprints) and to complete the list of involved genes. METHODS: An inflammation-centered cDNA microarray was used to screen mRNA expression in leukocytes of eight male athletes before (t0), immediately (t1) and 24h after (t2) a half marathon (HM). Differentially regulated gene expression was analyzed using a linear regression-based algorithm. Genes were clustered based upon similarity in gene expression changes with samples from t0 vs t1 and from t0 vs t2. The assumption for a gene to be clustered was that its regulation was identical in all eight athletes at the particular time. Selected genes were evaluated by quantitative Real Time PCR. RESULTS: Comparing t0 with t1, and t0 with t2, 36 and 21 genes respectively, were similarly regulated in all eight athletes. This pattern of identically changed genes can be viewed as a 'gene expression fingerprint' at the particular times post-exercise. Genes were allocated to functional groups such as signal transduction, cell type specific surface markers, cellular interaction and protection, apoptosis, and inflammatory processes. CONCLUSION: Microarray analysis is applicable for exercise-related gene expression profiling in human leukocytes. An exercise-related gene expression fingerprint may become helpful to characterize the immune response to different types of exercise or even to diagnose overtraining.
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